Overview

Dataset statistics

Number of variables29
Number of observations52694
Missing cells305808
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 MiB
Average record size in memory232.0 B

Variable types

Numeric10
Categorical19

Alerts

Registration_Date has a high cardinality: 1201 distinct valuesHigh cardinality
Education_Score has a high cardinality: 211 distinct valuesHigh cardinality
First_Interaction has a high cardinality: 1468 distinct valuesHigh cardinality
Health_Camp_ID is highly overall correlated with Camp_Start_Date and 3 other fieldsHigh correlation
Var1 is highly overall correlated with Var2 and 2 other fieldsHigh correlation
Var2 is highly overall correlated with Var1 and 2 other fieldsHigh correlation
Var5 is highly overall correlated with Var1 and 2 other fieldsHigh correlation
Donation is highly overall correlated with outcome and 1 other fieldsHigh correlation
Health_Score is highly overall correlated with outcome and 1 other fieldsHigh correlation
Health Score is highly overall correlated with outcome and 2 other fieldsHigh correlation
Number_of_stall_visited is highly overall correlated with Last_Stall_Visited_Number and 4 other fieldsHigh correlation
Last_Stall_Visited_Number is highly overall correlated with Number_of_stall_visited and 4 other fieldsHigh correlation
Var3 is highly overall correlated with Var1 and 3 other fieldsHigh correlation
outcome is highly overall correlated with Donation and 5 other fieldsHigh correlation
Online_Follower is highly overall correlated with Twitter_SharedHigh correlation
Twitter_Shared is highly overall correlated with Online_Follower and 1 other fieldsHigh correlation
Facebook_Shared is highly overall correlated with Twitter_SharedHigh correlation
Age is highly overall correlated with Var3High correlation
Camp_Start_Date is highly overall correlated with Health_Camp_ID and 5 other fieldsHigh correlation
Camp_End_Date is highly overall correlated with Health_Camp_ID and 4 other fieldsHigh correlation
Category1 is highly overall correlated with Health_Camp_ID and 8 other fieldsHigh correlation
Category2 is highly overall correlated with Health_Camp_ID and 5 other fieldsHigh correlation
Category3 is highly overall correlated with Health Score and 4 other fieldsHigh correlation
Var3 is highly imbalanced (99.5%)Imbalance
Var4 is highly imbalanced (94.2%)Imbalance
Online_Follower is highly imbalanced (69.0%)Imbalance
LinkedIn_Shared is highly imbalanced (65.2%)Imbalance
Twitter_Shared is highly imbalanced (70.1%)Imbalance
Facebook_Shared is highly imbalanced (69.2%)Imbalance
Education_Score is highly imbalanced (83.0%)Imbalance
Age is highly imbalanced (57.4%)Imbalance
Category3 is highly imbalanced (95.2%)Imbalance
City_Type has 23236 (44.1%) missing valuesMissing
Employer_Category has 42095 (79.9%) missing valuesMissing
Donation has 48337 (91.7%) missing valuesMissing
Health_Score has 48337 (91.7%) missing valuesMissing
Health Score has 47214 (89.6%) missing valuesMissing
Number_of_stall_visited has 48179 (91.4%) missing valuesMissing
Last_Stall_Visited_Number has 48179 (91.4%) missing valuesMissing
Var1 is highly skewed (γ1 = 20.76283178)Skewed
Var2 is highly skewed (γ1 = 26.92694979)Skewed
Var1 has 47284 (89.7%) zerosZeros
Var2 has 50989 (96.8%) zerosZeros
Var5 has 48546 (92.1%) zerosZeros

Reproduction

Analysis started2023-01-26 08:06:14.152718
Analysis finished2023-01-26 08:06:41.096093
Duration26.94 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Patient_ID
Real number (ℝ)

Distinct24632
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean507203.62
Minimum485679
Maximum528657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:41.248531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum485679
5-th percentile487795
Q1496444.25
median507241
Q3517919
95-th percentile526526.7
Maximum528657
Range42978
Interquartile range (IQR)21474.75

Descriptive statistics

Standard deviation12408.581
Coefficient of variation (CV)0.024464693
Kurtosis-1.1983697
Mean507203.62
Median Absolute Deviation (MAD)10725
Skewness-0.004342852
Sum2.6726588 × 1010
Variance1.5397288 × 108
MonotonicityNot monotonic
2023-01-26T13:36:41.607563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
516956 25
 
< 0.1%
509188 22
 
< 0.1%
490196 21
 
< 0.1%
513633 21
 
< 0.1%
502457 21
 
< 0.1%
496296 20
 
< 0.1%
517006 19
 
< 0.1%
495998 19
 
< 0.1%
512069 19
 
< 0.1%
492396 18
 
< 0.1%
Other values (24622) 52489
99.6%
ValueCountFrequency (%)
485679 2
< 0.1%
485681 1
< 0.1%
485685 1
< 0.1%
485686 1
< 0.1%
485690 2
< 0.1%
485691 1
< 0.1%
485697 1
< 0.1%
485698 2
< 0.1%
485699 1
< 0.1%
485701 1
< 0.1%
ValueCountFrequency (%)
528657 5
< 0.1%
528655 2
 
< 0.1%
528650 2
 
< 0.1%
528649 3
< 0.1%
528648 1
 
< 0.1%
528647 3
< 0.1%
528646 1
 
< 0.1%
528645 3
< 0.1%
528643 1
 
< 0.1%
528642 1
 
< 0.1%

Health_Camp_ID
Real number (ℝ)

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6547.5982
Minimum6523
Maximum6587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:41.739681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6523
5-th percentile6526
Q16534
median6541
Q36562
95-th percentile6585
Maximum6587
Range64
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.265787
Coefficient of variation (CV)0.0029424204
Kurtosis-0.87012188
Mean6547.5982
Median Absolute Deviation (MAD)12
Skewness0.70791228
Sum3.4501914 × 108
Variance371.17053
MonotonicityNot monotonic
2023-01-26T13:36:41.880726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
6543 4617
 
8.8%
6527 2865
 
5.4%
6538 2742
 
5.2%
6537 2716
 
5.2%
6529 2662
 
5.1%
6526 2646
 
5.0%
6534 2529
 
4.8%
6570 2520
 
4.8%
6580 2450
 
4.6%
6578 1966
 
3.7%
Other values (34) 24981
47.4%
ValueCountFrequency (%)
6523 1464
2.8%
6524 98
 
0.2%
6526 2646
5.0%
6527 2865
5.4%
6528 1245
2.4%
6529 2662
5.1%
6530 203
 
0.4%
6531 86
 
0.2%
6532 1449
2.7%
6534 2529
4.8%
ValueCountFrequency (%)
6587 47
 
0.1%
6586 1818
3.5%
6585 998
 
1.9%
6581 1055
2.0%
6580 2450
4.6%
6578 1966
3.7%
6575 63
 
0.1%
6571 1462
2.8%
6570 2520
4.8%
6569 134
 
0.3%
Distinct1201
Distinct (%)2.3%
Missing231
Missing (%)0.4%
Memory size411.8 KiB
28/03/06
 
611
08/05/05
 
407
31/03/06
 
357
24/12/04
 
342
05/01/05
 
331
Other values (1196)
50415 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters419704
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st row14/05/05
2nd row26/05/06
3rd row07/01/04
4th row12/02/04
5th row14/03/04

Common Values

ValueCountFrequency (%)
28/03/06 611
 
1.2%
08/05/05 407
 
0.8%
31/03/06 357
 
0.7%
24/12/04 342
 
0.6%
05/01/05 331
 
0.6%
24/05/05 326
 
0.6%
14/05/05 324
 
0.6%
30/03/06 305
 
0.6%
18/06/05 290
 
0.6%
07/05/05 256
 
0.5%
Other values (1191) 48914
92.8%

Length

2023-01-26T13:36:42.040679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28/03/06 611
 
1.2%
08/05/05 407
 
0.8%
31/03/06 357
 
0.7%
24/12/04 342
 
0.7%
05/01/05 331
 
0.6%
24/05/05 326
 
0.6%
14/05/05 324
 
0.6%
30/03/06 305
 
0.6%
18/06/05 290
 
0.6%
07/05/05 256
 
0.5%
Other values (1191) 48914
93.2%

Most occurring characters

ValueCountFrequency (%)
0 115669
27.6%
/ 104926
25.0%
1 47449
11.3%
5 36623
 
8.7%
2 31268
 
7.5%
4 21655
 
5.2%
6 20644
 
4.9%
3 14787
 
3.5%
8 9504
 
2.3%
9 8861
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314778
75.0%
Other Punctuation 104926
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115669
36.7%
1 47449
15.1%
5 36623
 
11.6%
2 31268
 
9.9%
4 21655
 
6.9%
6 20644
 
6.6%
3 14787
 
4.7%
8 9504
 
3.0%
9 8861
 
2.8%
7 8318
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 104926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 115669
27.6%
/ 104926
25.0%
1 47449
11.3%
5 36623
 
8.7%
2 31268
 
7.5%
4 21655
 
5.2%
6 20644
 
4.9%
3 14787
 
3.5%
8 9504
 
2.3%
9 8861
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 115669
27.6%
/ 104926
25.0%
1 47449
11.3%
5 36623
 
8.7%
2 31268
 
7.5%
4 21655
 
5.2%
6 20644
 
4.9%
3 14787
 
3.5%
8 9504
 
2.3%
9 8861
 
2.1%

Var1
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct120
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84026644
Minimum0
Maximum288
Zeros47284
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:42.141732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum288
Range288
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.0819472
Coefficient of variation (CV)9.6183148
Kurtosis510.74627
Mean0.84026644
Median Absolute Deviation (MAD)0
Skewness20.762832
Sum44277
Variance65.31787
MonotonicityNot monotonic
2023-01-26T13:36:42.313970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47284
89.7%
1 1733
 
3.3%
2 971
 
1.8%
3 756
 
1.4%
4 378
 
0.7%
5 263
 
0.5%
6 150
 
0.3%
7 134
 
0.3%
8 99
 
0.2%
9 94
 
0.2%
Other values (110) 832
 
1.6%
ValueCountFrequency (%)
0 47284
89.7%
1 1733
 
3.3%
2 971
 
1.8%
3 756
 
1.4%
4 378
 
0.7%
5 263
 
0.5%
6 150
 
0.3%
7 134
 
0.3%
8 99
 
0.2%
9 94
 
0.2%
ValueCountFrequency (%)
288 2
< 0.1%
286 1
< 0.1%
277 1
< 0.1%
271 1
< 0.1%
259 1
< 0.1%
252 1
< 0.1%
243 1
< 0.1%
238 2
< 0.1%
236 1
< 0.1%
227 1
< 0.1%

Var2
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25849243
Minimum0
Maximum156
Zeros50989
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:42.436665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum156
Range156
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9826786
Coefficient of variation (CV)15.407332
Kurtosis822.24522
Mean0.25849243
Median Absolute Deviation (MAD)0
Skewness26.92695
Sum13621
Variance15.861729
MonotonicityNot monotonic
2023-01-26T13:36:42.582759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50989
96.8%
1 680
 
1.3%
2 358
 
0.7%
3 190
 
0.4%
4 70
 
0.1%
5 54
 
0.1%
6 41
 
0.1%
9 29
 
0.1%
7 23
 
< 0.1%
10 22
 
< 0.1%
Other values (61) 238
 
0.5%
ValueCountFrequency (%)
0 50989
96.8%
1 680
 
1.3%
2 358
 
0.7%
3 190
 
0.4%
4 70
 
0.1%
5 54
 
0.1%
6 41
 
0.1%
7 23
 
< 0.1%
8 14
 
< 0.1%
9 29
 
0.1%
ValueCountFrequency (%)
156 1
 
< 0.1%
150 2
 
< 0.1%
148 1
 
< 0.1%
147 4
< 0.1%
141 1
 
< 0.1%
138 1
 
< 0.1%
133 2
 
< 0.1%
131 2
 
< 0.1%
129 1
 
< 0.1%
123 8
< 0.1%

Var3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
0
52672 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 52672
> 99.9%
1 22
 
< 0.1%

Length

2023-01-26T13:36:42.694024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:42.795920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 52672
> 99.9%
1 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 52672
> 99.9%
1 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52672
> 99.9%
1 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52672
> 99.9%
1 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52672
> 99.9%
1 22
 
< 0.1%

Var4
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
0
51891 
1
 
503
2
 
226
3
 
63
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51891
98.5%
1 503
 
1.0%
2 226
 
0.4%
3 63
 
0.1%
4 11
 
< 0.1%

Length

2023-01-26T13:36:42.914981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:43.016140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 51891
98.5%
1 503
 
1.0%
2 226
 
0.4%
3 63
 
0.1%
4 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 51891
98.5%
1 503
 
1.0%
2 226
 
0.4%
3 63
 
0.1%
4 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51891
98.5%
1 503
 
1.0%
2 226
 
0.4%
3 63
 
0.1%
4 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51891
98.5%
1 503
 
1.0%
2 226
 
0.4%
3 63
 
0.1%
4 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51891
98.5%
1 503
 
1.0%
2 226
 
0.4%
3 63
 
0.1%
4 11
 
< 0.1%

Var5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2531218
Minimum0
Maximum31
Zeros48546
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:43.114988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2510854
Coefficient of variation (CV)4.9426223
Kurtosis115.60937
Mean0.2531218
Median Absolute Deviation (MAD)0
Skewness8.9185138
Sum13338
Variance1.5652148
MonotonicityNot monotonic
2023-01-26T13:36:43.251200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 48546
92.1%
1 1481
 
2.8%
2 791
 
1.5%
3 654
 
1.2%
4 375
 
0.7%
5 231
 
0.4%
6 155
 
0.3%
7 146
 
0.3%
8 102
 
0.2%
10 44
 
0.1%
Other values (20) 169
 
0.3%
ValueCountFrequency (%)
0 48546
92.1%
1 1481
 
2.8%
2 791
 
1.5%
3 654
 
1.2%
4 375
 
0.7%
5 231
 
0.4%
6 155
 
0.3%
7 146
 
0.3%
8 102
 
0.2%
9 41
 
0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
29 3
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 4
< 0.1%
23 1
 
< 0.1%
22 8
< 0.1%
21 3
 
< 0.1%
20 9
< 0.1%

outcome
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
0
38354 
1
14340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38354
72.8%
1 14340
 
27.2%

Length

2023-01-26T13:36:43.360520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:43.479229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 38354
72.8%
1 14340
 
27.2%

Most occurring characters

ValueCountFrequency (%)
0 38354
72.8%
1 14340
 
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38354
72.8%
1 14340
 
27.2%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38354
72.8%
1 14340
 
27.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38354
72.8%
1 14340
 
27.2%

Online_Follower
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
0
49762 
1
 
2932

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49762
94.4%
1 2932
 
5.6%

Length

2023-01-26T13:36:43.555700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:43.656051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49762
94.4%
1 2932
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 49762
94.4%
1 2932
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49762
94.4%
1 2932
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49762
94.4%
1 2932
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49762
94.4%
1 2932
 
5.6%

LinkedIn_Shared
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
0
49258 
1
 
3436

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49258
93.5%
1 3436
 
6.5%

Length

2023-01-26T13:36:43.758221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:43.847256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49258
93.5%
1 3436
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 49258
93.5%
1 3436
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49258
93.5%
1 3436
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49258
93.5%
1 3436
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49258
93.5%
1 3436
 
6.5%

Twitter_Shared
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
0
49904 
1
 
2790

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49904
94.7%
1 2790
 
5.3%

Length

2023-01-26T13:36:43.935504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:44.060128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49904
94.7%
1 2790
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 49904
94.7%
1 2790
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49904
94.7%
1 2790
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49904
94.7%
1 2790
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49904
94.7%
1 2790
 
5.3%

Facebook_Shared
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
0
49787 
1
 
2907

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49787
94.5%
1 2907
 
5.5%

Length

2023-01-26T13:36:44.140421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:44.267702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49787
94.5%
1 2907
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 49787
94.5%
1 2907
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49787
94.5%
1 2907
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49787
94.5%
1 2907
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49787
94.5%
1 2907
 
5.5%

Income
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
None
37496 
0
5882 
1
 
3726
2
 
2830
3
 
1535
Other values (3)
 
1225

Length

Max length4
Median length4
Mean length3.1347402
Min length1

Characters and Unicode

Total characters165182
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 37496
71.2%
0 5882
 
11.2%
1 3726
 
7.1%
2 2830
 
5.4%
3 1535
 
2.9%
4 743
 
1.4%
5 300
 
0.6%
6 182
 
0.3%

Length

2023-01-26T13:36:44.376779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:44.555007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
none 37496
71.2%
0 5882
 
11.2%
1 3726
 
7.1%
2 2830
 
5.4%
3 1535
 
2.9%
4 743
 
1.4%
5 300
 
0.6%
6 182
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N 37496
22.7%
o 37496
22.7%
n 37496
22.7%
e 37496
22.7%
0 5882
 
3.6%
1 3726
 
2.3%
2 2830
 
1.7%
3 1535
 
0.9%
4 743
 
0.4%
5 300
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112488
68.1%
Uppercase Letter 37496
 
22.7%
Decimal Number 15198
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5882
38.7%
1 3726
24.5%
2 2830
18.6%
3 1535
 
10.1%
4 743
 
4.9%
5 300
 
2.0%
6 182
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
o 37496
33.3%
n 37496
33.3%
e 37496
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 37496
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149984
90.8%
Common 15198
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5882
38.7%
1 3726
24.5%
2 2830
18.6%
3 1535
 
10.1%
4 743
 
4.9%
5 300
 
2.0%
6 182
 
1.2%
Latin
ValueCountFrequency (%)
N 37496
25.0%
o 37496
25.0%
n 37496
25.0%
e 37496
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 37496
22.7%
o 37496
22.7%
n 37496
22.7%
e 37496
22.7%
0 5882
 
3.6%
1 3726
 
2.3%
2 2830
 
1.7%
3 1535
 
0.9%
4 743
 
0.4%
5 300
 
0.2%

Education_Score
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct211
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
None
45711 
82
 
343
79
 
307
75
 
285
86
 
280
Other values (206)
5768 

Length

Max length11
Median length4
Mean length3.7983452
Min length2

Characters and Unicode

Total characters200150
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.1%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 45711
86.7%
82 343
 
0.7%
79 307
 
0.6%
75 285
 
0.5%
86 280
 
0.5%
87 267
 
0.5%
76 263
 
0.5%
77 245
 
0.5%
89 244
 
0.5%
80 244
 
0.5%
Other values (201) 4505
 
8.5%

Length

2023-01-26T13:36:44.712320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 45711
86.7%
82 343
 
0.7%
79 307
 
0.6%
75 285
 
0.5%
86 280
 
0.5%
87 267
 
0.5%
76 263
 
0.5%
77 245
 
0.5%
89 244
 
0.5%
80 244
 
0.5%
Other values (201) 4505
 
8.5%

Most occurring characters

ValueCountFrequency (%)
N 45711
22.8%
o 45711
22.8%
n 45711
22.8%
e 45711
22.8%
7 3477
 
1.7%
8 3403
 
1.7%
6 2569
 
1.3%
9 1547
 
0.8%
3 1485
 
0.7%
5 996
 
0.5%
Other values (5) 3829
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 137133
68.5%
Uppercase Letter 45711
 
22.8%
Decimal Number 16695
 
8.3%
Other Punctuation 611
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 3477
20.8%
8 3403
20.4%
6 2569
15.4%
9 1547
9.3%
3 1485
8.9%
5 996
 
6.0%
2 952
 
5.7%
0 817
 
4.9%
4 730
 
4.4%
1 719
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
o 45711
33.3%
n 45711
33.3%
e 45711
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 45711
100.0%
Other Punctuation
ValueCountFrequency (%)
. 611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 182844
91.4%
Common 17306
 
8.6%

Most frequent character per script

Common
ValueCountFrequency (%)
7 3477
20.1%
8 3403
19.7%
6 2569
14.8%
9 1547
8.9%
3 1485
8.6%
5 996
 
5.8%
2 952
 
5.5%
0 817
 
4.7%
4 730
 
4.2%
1 719
 
4.2%
Latin
ValueCountFrequency (%)
N 45711
25.0%
o 45711
25.0%
n 45711
25.0%
e 45711
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 45711
22.8%
o 45711
22.8%
n 45711
22.8%
e 45711
22.8%
7 3477
 
1.7%
8 3403
 
1.7%
6 2569
 
1.3%
9 1547
 
0.8%
3 1485
 
0.7%
5 996
 
0.5%
Other values (5) 3829
 
1.9%

Age
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
None
36108 
41
 
1273
40
 
1210
42
 
1186
43
 
1151
Other values (45)
11766 

Length

Max length4
Median length4
Mean length3.3704786
Min length2

Characters and Unicode

Total characters177604
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 36108
68.5%
41 1273
 
2.4%
40 1210
 
2.3%
42 1186
 
2.3%
43 1151
 
2.2%
39 1036
 
2.0%
44 1021
 
1.9%
45 778
 
1.5%
46 734
 
1.4%
37 732
 
1.4%
Other values (40) 7465
 
14.2%

Length

2023-01-26T13:36:44.838416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 36108
68.5%
41 1273
 
2.4%
40 1210
 
2.3%
42 1186
 
2.3%
43 1151
 
2.2%
39 1036
 
2.0%
44 1021
 
1.9%
45 778
 
1.5%
46 734
 
1.4%
37 732
 
1.4%
Other values (40) 7465
 
14.2%

Most occurring characters

ValueCountFrequency (%)
N 36108
20.3%
o 36108
20.3%
n 36108
20.3%
e 36108
20.3%
4 10673
 
6.0%
3 5183
 
2.9%
7 3888
 
2.2%
5 2959
 
1.7%
2 2060
 
1.2%
1 2005
 
1.1%
Other values (4) 6404
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 108324
61.0%
Uppercase Letter 36108
 
20.3%
Decimal Number 33172
 
18.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 10673
32.2%
3 5183
15.6%
7 3888
 
11.7%
5 2959
 
8.9%
2 2060
 
6.2%
1 2005
 
6.0%
0 1814
 
5.5%
9 1643
 
5.0%
6 1631
 
4.9%
8 1316
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
o 36108
33.3%
n 36108
33.3%
e 36108
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 36108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 144432
81.3%
Common 33172
 
18.7%

Most frequent character per script

Common
ValueCountFrequency (%)
4 10673
32.2%
3 5183
15.6%
7 3888
 
11.7%
5 2959
 
8.9%
2 2060
 
6.2%
1 2005
 
6.0%
0 1814
 
5.5%
9 1643
 
5.0%
6 1631
 
4.9%
8 1316
 
4.0%
Latin
ValueCountFrequency (%)
N 36108
25.0%
o 36108
25.0%
n 36108
25.0%
e 36108
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 36108
20.3%
o 36108
20.3%
n 36108
20.3%
e 36108
20.3%
4 10673
 
6.0%
3 5183
 
2.9%
7 3888
 
2.2%
5 2959
 
1.7%
2 2060
 
1.2%
1 2005
 
1.1%
Other values (4) 6404
 
3.6%
Distinct1468
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
08-Sep-04
 
1064
01-May-05
 
870
08-Feb-03
 
687
21-May-05
 
635
25-Oct-04
 
610
Other values (1463)
48828 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters474246
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.1%

Sample

1st row14-Nov-04
2nd row26-May-06
3rd row07-Jan-04
4th row12-Feb-04
5th row14-Mar-04

Common Values

ValueCountFrequency (%)
08-Sep-04 1064
 
2.0%
01-May-05 870
 
1.7%
08-Feb-03 687
 
1.3%
21-May-05 635
 
1.2%
25-Oct-04 610
 
1.2%
09-Feb-05 567
 
1.1%
03-Oct-04 528
 
1.0%
11-Dec-04 507
 
1.0%
21-Sep-04 485
 
0.9%
10-May-05 437
 
0.8%
Other values (1458) 46304
87.9%

Length

2023-01-26T13:36:44.972736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08-sep-04 1064
 
2.0%
01-may-05 870
 
1.7%
08-feb-03 687
 
1.3%
21-may-05 635
 
1.2%
25-oct-04 610
 
1.2%
09-feb-05 567
 
1.1%
03-oct-04 528
 
1.0%
11-dec-04 507
 
1.0%
21-sep-04 485
 
0.9%
10-may-05 437
 
0.8%
Other values (1458) 46304
87.9%

Most occurring characters

ValueCountFrequency (%)
- 105388
22.2%
0 74974
15.8%
1 23358
 
4.9%
4 22788
 
4.8%
2 21914
 
4.6%
5 21741
 
4.6%
3 18237
 
3.8%
e 14010
 
3.0%
a 13657
 
2.9%
J 13151
 
2.8%
Other values (23) 145028
30.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210776
44.4%
Dash Punctuation 105388
22.2%
Lowercase Letter 105388
22.2%
Uppercase Letter 52694
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14010
13.3%
a 13657
13.0%
u 11934
11.3%
n 9917
9.4%
c 9794
9.3%
p 7848
7.4%
r 6963
6.6%
t 4915
 
4.7%
y 4866
 
4.6%
b 4784
 
4.5%
Other values (4) 16700
15.8%
Decimal Number
ValueCountFrequency (%)
0 74974
35.6%
1 23358
 
11.1%
4 22788
 
10.8%
2 21914
 
10.4%
5 21741
 
10.3%
3 18237
 
8.7%
6 11075
 
5.3%
8 6860
 
3.3%
7 5048
 
2.4%
9 4781
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
J 13151
25.0%
M 8328
15.8%
A 7613
14.4%
O 4915
 
9.3%
D 4879
 
9.3%
F 4784
 
9.1%
N 4677
 
8.9%
S 4347
 
8.2%
Dash Punctuation
ValueCountFrequency (%)
- 105388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 316164
66.7%
Latin 158082
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14010
 
8.9%
a 13657
 
8.6%
J 13151
 
8.3%
u 11934
 
7.5%
n 9917
 
6.3%
c 9794
 
6.2%
M 8328
 
5.3%
p 7848
 
5.0%
A 7613
 
4.8%
r 6963
 
4.4%
Other values (12) 54867
34.7%
Common
ValueCountFrequency (%)
- 105388
33.3%
0 74974
23.7%
1 23358
 
7.4%
4 22788
 
7.2%
2 21914
 
6.9%
5 21741
 
6.9%
3 18237
 
5.8%
6 11075
 
3.5%
8 6860
 
2.2%
7 5048
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 105388
22.2%
0 74974
15.8%
1 23358
 
4.9%
4 22788
 
4.8%
2 21914
 
4.6%
5 21741
 
4.6%
3 18237
 
3.8%
e 14010
 
3.0%
a 13657
 
2.9%
J 13151
 
2.8%
Other values (23) 145028
30.6%

City_Type
Categorical

Distinct9
Distinct (%)< 0.1%
Missing23236
Missing (%)44.1%
Memory size411.8 KiB
B
5814 
H
4267 
D
3811 
G
2990 
C
2962 
Other values (4)
9614 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29458
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowH
2nd rowH
3rd rowB
4th rowB
5th rowG

Common Values

ValueCountFrequency (%)
B 5814
 
11.0%
H 4267
 
8.1%
D 3811
 
7.2%
G 2990
 
5.7%
C 2962
 
5.6%
E 2849
 
5.4%
A 2413
 
4.6%
I 2370
 
4.5%
F 1982
 
3.8%
(Missing) 23236
44.1%

Length

2023-01-26T13:36:45.095566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:45.259942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
b 5814
19.7%
h 4267
14.5%
d 3811
12.9%
g 2990
10.2%
c 2962
10.1%
e 2849
9.7%
a 2413
8.2%
i 2370
8.0%
f 1982
 
6.7%

Most occurring characters

ValueCountFrequency (%)
B 5814
19.7%
H 4267
14.5%
D 3811
12.9%
G 2990
10.2%
C 2962
10.1%
E 2849
9.7%
A 2413
8.2%
I 2370
8.0%
F 1982
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29458
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 5814
19.7%
H 4267
14.5%
D 3811
12.9%
G 2990
10.2%
C 2962
10.1%
E 2849
9.7%
A 2413
8.2%
I 2370
8.0%
F 1982
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 29458
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 5814
19.7%
H 4267
14.5%
D 3811
12.9%
G 2990
10.2%
C 2962
10.1%
E 2849
9.7%
A 2413
8.2%
I 2370
8.0%
F 1982
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 5814
19.7%
H 4267
14.5%
D 3811
12.9%
G 2990
10.2%
C 2962
10.1%
E 2849
9.7%
A 2413
8.2%
I 2370
8.0%
F 1982
 
6.7%
Distinct14
Distinct (%)0.1%
Missing42095
Missing (%)79.9%
Memory size411.8 KiB
Technology
2505 
Software Industry
1645 
Others
1537 
Consulting
1508 
Education
774 
Other values (9)
2630 

Length

Max length17
Median length13
Mean length9.8138504
Min length4

Characters and Unicode

Total characters104017
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBroadcasting
2nd rowBroadcasting
3rd rowEducation
4th rowRetail
5th rowBFSI

Common Values

ValueCountFrequency (%)
Technology 2505
 
4.8%
Software Industry 1645
 
3.1%
Others 1537
 
2.9%
Consulting 1508
 
2.9%
Education 774
 
1.5%
BFSI 569
 
1.1%
Retail 360
 
0.7%
Manufacturing 360
 
0.7%
Health 309
 
0.6%
Transport 256
 
0.5%
Other values (4) 776
 
1.5%
(Missing) 42095
79.9%

Length

2023-01-26T13:36:45.665846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
technology 2505
20.0%
software 1645
13.2%
industry 1645
13.2%
others 1537
12.3%
consulting 1508
12.1%
education 774
 
6.2%
bfsi 569
 
4.6%
retail 360
 
2.9%
manufacturing 360
 
2.9%
health 309
 
2.5%
Other values (6) 1286
10.3%

Most occurring characters

ValueCountFrequency (%)
o 9931
 
9.5%
n 9048
 
8.7%
t 9034
 
8.7%
e 7212
 
6.9%
r 5831
 
5.6%
s 5332
 
5.1%
l 5110
 
4.9%
a 4836
 
4.6%
u 4647
 
4.5%
g 4505
 
4.3%
Other values (21) 38531
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87913
84.5%
Uppercase Letter 14205
 
13.7%
Space Separator 1899
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9931
11.3%
n 9048
10.3%
t 9034
10.3%
e 7212
 
8.2%
r 5831
 
6.6%
s 5332
 
6.1%
l 5110
 
5.8%
a 4836
 
5.5%
u 4647
 
5.3%
g 4505
 
5.1%
Other values (9) 22427
25.5%
Uppercase Letter
ValueCountFrequency (%)
T 2935
20.7%
I 2214
15.6%
S 2214
15.6%
O 1537
10.8%
C 1508
10.6%
E 1028
 
7.2%
F 785
 
5.5%
B 701
 
4.9%
R 614
 
4.3%
M 360
 
2.5%
Space Separator
ValueCountFrequency (%)
1899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102118
98.2%
Common 1899
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 9931
 
9.7%
n 9048
 
8.9%
t 9034
 
8.8%
e 7212
 
7.1%
r 5831
 
5.7%
s 5332
 
5.2%
l 5110
 
5.0%
a 4836
 
4.7%
u 4647
 
4.6%
g 4505
 
4.4%
Other values (20) 36632
35.9%
Common
ValueCountFrequency (%)
1899
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 9931
 
9.5%
n 9048
 
8.7%
t 9034
 
8.7%
e 7212
 
6.9%
r 5831
 
5.6%
s 5332
 
5.1%
l 5110
 
4.9%
a 4836
 
4.6%
u 4647
 
4.5%
g 4505
 
4.3%
Other values (21) 38531
37.0%

Camp_Start_Date
Categorical

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
27-Sep-05
7333 
19-Feb-05
 
3074
09-Jan-04
 
3025
13-Jun-05
 
2865
30-Mar-06
 
2662
Other values (35)
33735 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters474246
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row09-Jul-05
2nd row17-Oct-05
3rd row04-Jan-04
4th row01-Feb-04
5th row07-Dec-03

Common Values

ValueCountFrequency (%)
27-Sep-05 7333
 
13.9%
19-Feb-05 3074
 
5.8%
09-Jan-04 3025
 
5.7%
13-Jun-05 2865
 
5.4%
30-Mar-06 2662
 
5.1%
03-Jan-05 2646
 
5.0%
17-Oct-05 2529
 
4.8%
09-Jul-05 2520
 
4.8%
22-Dec-04 2450
 
4.6%
16-Aug-05 1966
 
3.7%
Other values (30) 21624
41.0%

Length

2023-01-26T13:36:45.787247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27-sep-05 7333
 
13.9%
19-feb-05 3074
 
5.8%
09-jan-04 3025
 
5.7%
13-jun-05 2865
 
5.4%
30-mar-06 2662
 
5.1%
03-jan-05 2646
 
5.0%
17-oct-05 2529
 
4.8%
09-jul-05 2520
 
4.8%
22-dec-04 2450
 
4.6%
16-aug-05 1966
 
3.7%
Other values (30) 21624
41.0%

Most occurring characters

ValueCountFrequency (%)
- 105388
22.2%
0 74390
15.7%
5 35286
 
7.4%
e 22308
 
4.7%
1 21042
 
4.4%
2 18984
 
4.0%
3 15240
 
3.2%
4 14893
 
3.1%
J 14016
 
3.0%
7 12752
 
2.7%
Other values (22) 139947
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210776
44.4%
Dash Punctuation 105388
22.2%
Lowercase Letter 105388
22.2%
Uppercase Letter 52694
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22308
21.2%
u 12002
11.4%
n 11496
10.9%
c 10874
10.3%
p 8899
 
8.4%
b 8521
 
8.1%
a 8517
 
8.1%
t 5872
 
5.6%
v 3881
 
3.7%
o 3881
 
3.7%
Other values (4) 9137
8.7%
Decimal Number
ValueCountFrequency (%)
0 74390
35.3%
5 35286
16.7%
1 21042
 
10.0%
2 18984
 
9.0%
3 15240
 
7.2%
4 14893
 
7.1%
7 12752
 
6.1%
9 11958
 
5.7%
6 6231
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
J 14016
26.6%
S 8785
16.7%
F 8521
16.2%
O 5872
11.1%
D 5002
 
9.5%
N 3881
 
7.4%
A 3805
 
7.2%
M 2812
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 105388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 316164
66.7%
Latin 158082
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22308
14.1%
J 14016
 
8.9%
u 12002
 
7.6%
n 11496
 
7.3%
c 10874
 
6.9%
p 8899
 
5.6%
S 8785
 
5.6%
b 8521
 
5.4%
F 8521
 
5.4%
a 8517
 
5.4%
Other values (12) 44143
27.9%
Common
ValueCountFrequency (%)
- 105388
33.3%
0 74390
23.5%
5 35286
 
11.2%
1 21042
 
6.7%
2 18984
 
6.0%
3 15240
 
4.8%
4 14893
 
4.7%
7 12752
 
4.0%
9 11958
 
3.8%
6 6231
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 105388
22.2%
0 74390
15.7%
5 35286
 
7.4%
e 22308
 
4.7%
1 21042
 
4.4%
2 18984
 
4.0%
3 15240
 
3.2%
4 14893
 
3.1%
J 14016
 
3.0%
7 12752
 
2.7%
Other values (22) 139947
29.5%

Camp_End_Date
Categorical

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
07-Nov-07
9862 
22-Jul-05
5385 
23-Aug-05
3074 
04-Feb-05
 
2742
03-Apr-06
 
2662
Other values (33)
28969 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters474246
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22-Jul-05
2nd row07-Nov-07
3rd row09-Jan-04
4th row18-Feb-04
5th row13-Jun-04

Common Values

ValueCountFrequency (%)
07-Nov-07 9862
18.7%
22-Jul-05 5385
 
10.2%
23-Aug-05 3074
 
5.8%
04-Feb-05 2742
 
5.2%
03-Apr-06 2662
 
5.1%
20-Feb-05 2646
 
5.0%
06-Jan-05 2450
 
4.6%
14-Oct-05 2029
 
3.9%
18-Oct-04 1818
 
3.5%
02-Jun-05 1659
 
3.1%
Other values (28) 18367
34.9%

Length

2023-01-26T13:36:45.873088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07-nov-07 9862
18.7%
22-jul-05 5385
 
10.2%
23-aug-05 3074
 
5.8%
04-feb-05 2742
 
5.2%
03-apr-06 2662
 
5.1%
20-feb-05 2646
 
5.0%
06-jan-05 2450
 
4.6%
14-oct-05 2029
 
3.9%
18-oct-04 1818
 
3.5%
02-jun-05 1659
 
3.1%
Other values (28) 18367
34.9%

Most occurring characters

ValueCountFrequency (%)
- 105388
22.2%
0 79822
16.8%
5 32290
 
6.8%
2 24110
 
5.1%
7 21331
 
4.5%
1 16259
 
3.4%
J 15203
 
3.2%
u 14604
 
3.1%
e 13622
 
2.9%
4 12517
 
2.6%
Other values (23) 139100
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210776
44.4%
Dash Punctuation 105388
22.2%
Lowercase Letter 105388
22.2%
Uppercase Letter 52694
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 14604
13.9%
e 13622
12.9%
o 10949
10.4%
v 10949
10.4%
b 9485
9.0%
n 8184
7.8%
l 7019
6.7%
p 6823
6.5%
c 6733
6.4%
t 5512
 
5.2%
Other values (4) 11508
10.9%
Decimal Number
ValueCountFrequency (%)
0 79822
37.9%
5 32290
15.3%
2 24110
 
11.4%
7 21331
 
10.1%
1 16259
 
7.7%
4 12517
 
5.9%
3 9376
 
4.4%
6 8890
 
4.2%
8 4896
 
2.3%
9 1285
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
J 15203
28.9%
N 10949
20.8%
F 9485
18.0%
A 7144
13.6%
O 5512
 
10.5%
S 2916
 
5.5%
D 1221
 
2.3%
M 264
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 105388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 316164
66.7%
Latin 158082
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 15203
 
9.6%
u 14604
 
9.2%
e 13622
 
8.6%
o 10949
 
6.9%
v 10949
 
6.9%
N 10949
 
6.9%
F 9485
 
6.0%
b 9485
 
6.0%
n 8184
 
5.2%
A 7144
 
4.5%
Other values (12) 47508
30.1%
Common
ValueCountFrequency (%)
- 105388
33.3%
0 79822
25.2%
5 32290
 
10.2%
2 24110
 
7.6%
7 21331
 
6.7%
1 16259
 
5.1%
4 12517
 
4.0%
3 9376
 
3.0%
6 8890
 
2.8%
8 4896
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 105388
22.2%
0 79822
16.8%
5 32290
 
6.8%
2 24110
 
5.1%
7 21331
 
4.5%
1 16259
 
3.4%
J 15203
 
3.2%
u 14604
 
3.1%
e 13622
 
2.9%
4 12517
 
2.6%
Other values (23) 139100
29.3%

Category1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
First
34990 
Second
10559 
Third
7145 

Length

Max length6
Median length5
Mean length5.2003833
Min length5

Characters and Unicode

Total characters274029
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst
2nd rowSecond
3rd rowFirst
4th rowFirst
5th rowFirst

Common Values

ValueCountFrequency (%)
First 34990
66.4%
Second 10559
 
20.0%
Third 7145
 
13.6%

Length

2023-01-26T13:36:45.984294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:46.122606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
first 34990
66.4%
second 10559
 
20.0%
third 7145
 
13.6%

Most occurring characters

ValueCountFrequency (%)
i 42135
15.4%
r 42135
15.4%
F 34990
12.8%
s 34990
12.8%
t 34990
12.8%
d 17704
6.5%
S 10559
 
3.9%
e 10559
 
3.9%
c 10559
 
3.9%
o 10559
 
3.9%
Other values (3) 24849
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 221335
80.8%
Uppercase Letter 52694
 
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 42135
19.0%
r 42135
19.0%
s 34990
15.8%
t 34990
15.8%
d 17704
8.0%
e 10559
 
4.8%
c 10559
 
4.8%
o 10559
 
4.8%
n 10559
 
4.8%
h 7145
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
F 34990
66.4%
S 10559
 
20.0%
T 7145
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 274029
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 42135
15.4%
r 42135
15.4%
F 34990
12.8%
s 34990
12.8%
t 34990
12.8%
d 17704
6.5%
S 10559
 
3.9%
e 10559
 
3.9%
c 10559
 
3.9%
o 10559
 
3.9%
Other values (3) 24849
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 42135
15.4%
r 42135
15.4%
F 34990
12.8%
s 34990
12.8%
t 34990
12.8%
d 17704
6.5%
S 10559
 
3.9%
e 10559
 
3.9%
c 10559
 
3.9%
o 10559
 
3.9%
Other values (3) 24849
9.1%

Category2
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
F
17316 
E
14684 
A
7687 
G
7145 
D
2872 
Other values (2)
2990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowA
3rd rowC
4th rowE
5th rowF

Common Values

ValueCountFrequency (%)
F 17316
32.9%
E 14684
27.9%
A 7687
14.6%
G 7145
13.6%
D 2872
 
5.5%
B 1718
 
3.3%
C 1272
 
2.4%

Length

2023-01-26T13:36:46.228042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:46.345504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
f 17316
32.9%
e 14684
27.9%
a 7687
14.6%
g 7145
13.6%
d 2872
 
5.5%
b 1718
 
3.3%
c 1272
 
2.4%

Most occurring characters

ValueCountFrequency (%)
F 17316
32.9%
E 14684
27.9%
A 7687
14.6%
G 7145
13.6%
D 2872
 
5.5%
B 1718
 
3.3%
C 1272
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 52694
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 17316
32.9%
E 14684
27.9%
A 7687
14.6%
G 7145
13.6%
D 2872
 
5.5%
B 1718
 
3.3%
C 1272
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 52694
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 17316
32.9%
E 14684
27.9%
A 7687
14.6%
G 7145
13.6%
D 2872
 
5.5%
B 1718
 
3.3%
C 1272
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 17316
32.9%
E 14684
27.9%
A 7687
14.6%
G 7145
13.6%
D 2872
 
5.5%
B 1718
 
3.3%
C 1272
 
2.4%

Category3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
2
52416 
1
 
278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52694
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 52416
99.5%
1 278
 
0.5%

Length

2023-01-26T13:36:46.444139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:36:46.569442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2 52416
99.5%
1 278
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 52416
99.5%
1 278
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52694
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52416
99.5%
1 278
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 52694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52416
99.5%
1 278
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52416
99.5%
1 278
 
0.5%

Donation
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)0.5%
Missing48337
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean32.492541
Minimum10
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:46.650824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q120
median30
Q340
95-th percentile80
Maximum280
Range270
Interquartile range (IQR)20

Descriptive statistics

Standard deviation24.132949
Coefficient of variation (CV)0.74272274
Kurtosis10.114895
Mean32.492541
Median Absolute Deviation (MAD)10
Skewness2.3891015
Sum141570
Variance582.39922
MonotonicityNot monotonic
2023-01-26T13:36:46.742239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20 1110
 
2.1%
10 934
 
1.8%
30 885
 
1.7%
40 526
 
1.0%
50 342
 
0.6%
60 184
 
0.3%
70 146
 
0.3%
80 65
 
0.1%
90 49
 
0.1%
100 38
 
0.1%
Other values (11) 78
 
0.1%
(Missing) 48337
91.7%
ValueCountFrequency (%)
10 934
1.8%
20 1110
2.1%
30 885
1.7%
40 526
1.0%
50 342
 
0.6%
60 184
 
0.3%
70 146
 
0.3%
80 65
 
0.1%
90 49
 
0.1%
100 38
 
0.1%
ValueCountFrequency (%)
280 1
 
< 0.1%
250 1
 
< 0.1%
210 2
 
< 0.1%
180 2
 
< 0.1%
170 5
 
< 0.1%
160 5
 
< 0.1%
150 4
 
< 0.1%
140 12
< 0.1%
130 12
< 0.1%
120 13
< 0.1%

Health_Score
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2841
Distinct (%)65.2%
Missing48337
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean0.51643519
Minimum0.001666667
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:46.851781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001666667
5-th percentile0.057507987
Q10.25966851
median0.52697095
Q30.77166667
95-th percentile0.95636567
Maximum1
Range0.99833333
Interquartile range (IQR)0.51199816

Descriptive statistics

Standard deviation0.28926877
Coefficient of variation (CV)0.56012599
Kurtosis-1.2117525
Mean0.51643519
Median Absolute Deviation (MAD)0.25048569
Skewness-0.066340053
Sum2250.1081
Variance0.083676422
MonotonicityNot monotonic
2023-01-26T13:36:46.975471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.360338573 76
 
0.1%
0.46485623 71
 
0.1%
0.559854897 71
 
0.1%
0.753325272 51
 
0.1%
0.752136752 43
 
0.1%
0.887545345 42
 
0.1%
0.696428571 39
 
0.1%
0.436464088 39
 
0.1%
0.642172524 38
 
0.1%
0.779552716 34
 
0.1%
Other values (2831) 3853
 
7.3%
(Missing) 48337
91.7%
ValueCountFrequency (%)
0.001666667 1
< 0.1%
0.003333333 1
< 0.1%
0.003846154 1
< 0.1%
0.003937008 1
< 0.1%
0.003968254 1
< 0.1%
0.004149378 1
< 0.1%
0.004926108 1
< 0.1%
0.005 1
< 0.1%
0.007142857 2
< 0.1%
0.007220217 1
< 0.1%
ValueCountFrequency (%)
1 26
< 0.1%
0.99879081 1
 
< 0.1%
0.998402556 1
 
< 0.1%
0.998333333 1
 
< 0.1%
0.997925311 1
 
< 0.1%
0.99758162 2
 
< 0.1%
0.997237569 1
 
< 0.1%
0.996666667 1
 
< 0.1%
0.996183206 1
 
< 0.1%
0.996153846 1
 
< 0.1%

Health Score
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct205
Distinct (%)3.7%
Missing47214
Missing (%)89.6%
Infinite0
Infinite (%)0.0%
Mean0.55453795
Minimum0.058992806
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:47.123110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.058992806
5-th percentile0.11310592
Q10.38962606
median0.51879699
Q30.76974723
95-th percentile0.9644566
Maximum1
Range0.94100719
Interquartile range (IQR)0.38012118

Descriptive statistics

Standard deviation0.25110722
Coefficient of variation (CV)0.45282242
Kurtosis-0.93694133
Mean0.55453795
Median Absolute Deviation (MAD)0.17239722
Skewness0.031960567
Sum3038.868
Variance0.063054835
MonotonicityNot monotonic
2023-01-26T13:36:47.326378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.402053712 724
 
1.4%
0.373205742 377
 
0.7%
0.455371248 86
 
0.2%
0.505134281 83
 
0.2%
0.065741858 79
 
0.1%
0.572376357 76
 
0.1%
0.69119421 70
 
0.1%
0.747285887 69
 
0.1%
0.803377563 69
 
0.1%
0.507840772 69
 
0.1%
Other values (195) 3778
 
7.2%
(Missing) 47214
89.6%
ValueCountFrequency (%)
0.058992806 27
 
0.1%
0.065741858 79
0.1%
0.084892086 15
 
< 0.1%
0.099280576 3
 
< 0.1%
0.102062975 67
0.1%
0.103136309 41
0.1%
0.113105925 50
0.1%
0.11942446 11
 
< 0.1%
0.125452352 28
 
0.1%
0.127035831 18
 
< 0.1%
ValueCountFrequency (%)
1 6
< 0.1%
0.999605055 1
 
< 0.1%
0.999396864 2
 
< 0.1%
0.999316473 1
 
< 0.1%
0.998815166 1
 
< 0.1%
0.998632946 1
 
< 0.1%
0.998190591 3
< 0.1%
0.998025276 2
 
< 0.1%
0.997949419 1
 
< 0.1%
0.997828447 2
 
< 0.1%

Number_of_stall_visited
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.2%
Missing48179
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean2.9158361
Minimum0
Maximum7
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:47.446651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q34
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6791891
Coefficient of variation (CV)0.57588597
Kurtosis-1.1108093
Mean2.9158361
Median Absolute Deviation (MAD)2
Skewness0.39593218
Sum13165
Variance2.8196761
MonotonicityNot monotonic
2023-01-26T13:36:47.579937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1257
 
2.4%
2 884
 
1.7%
3 764
 
1.4%
5 718
 
1.4%
4 517
 
1.0%
6 351
 
0.7%
7 12
 
< 0.1%
0 12
 
< 0.1%
(Missing) 48179
91.4%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 1257
2.4%
2 884
1.7%
3 764
1.4%
4 517
1.0%
5 718
1.4%
6 351
 
0.7%
7 12
 
< 0.1%
ValueCountFrequency (%)
7 12
 
< 0.1%
6 351
 
0.7%
5 718
1.4%
4 517
1.0%
3 764
1.4%
2 884
1.7%
1 1257
2.4%
0 12
 
< 0.1%

Last_Stall_Visited_Number
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.2%
Missing48179
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean2.4013289
Minimum0
Maximum7
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size411.8 KiB
2023-01-26T13:36:47.700674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4635425
Coefficient of variation (CV)0.6094719
Kurtosis-0.34187765
Mean2.4013289
Median Absolute Deviation (MAD)1
Skewness0.78695724
Sum10842
Variance2.1419566
MonotonicityNot monotonic
2023-01-26T13:36:47.822313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1728
 
3.3%
2 897
 
1.7%
3 855
 
1.6%
4 534
 
1.0%
5 320
 
0.6%
6 164
 
0.3%
0 12
 
< 0.1%
7 5
 
< 0.1%
(Missing) 48179
91.4%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 1728
3.3%
2 897
1.7%
3 855
1.6%
4 534
 
1.0%
5 320
 
0.6%
6 164
 
0.3%
7 5
 
< 0.1%
ValueCountFrequency (%)
7 5
 
< 0.1%
6 164
 
0.3%
5 320
 
0.6%
4 534
 
1.0%
3 855
1.6%
2 897
1.7%
1 1728
3.3%
0 12
 
< 0.1%

Interactions

2023-01-26T13:36:37.671321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:23.931661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:25.752795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:27.354102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:28.770484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:30.430608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:32.021103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:33.421944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.775630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:36.283290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:37.819438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:24.119340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:25.870098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:27.482520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:28.920642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:30.618363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:32.160454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:33.537570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.885194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:36.450063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:38.004813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:24.450027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:26.082582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:27.659946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:29.062074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:30.785651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:32.366353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:33.683821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:35.046611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:36.598898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:38.127434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:24.606852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:26.245788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:27.827909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:29.243414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:30.910717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:32.490694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:33.863161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:35.365005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:36.724545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:38.303645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:24.854415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:26.454529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:27.968682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:29.398709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:31.108713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:32.608334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.016913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:35.495418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:36.893196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:38.459982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:24.994802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:26.616630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:28.100916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:29.525472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:31.265141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:32.765014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.162905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:35.621605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:37.023962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:38.605654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:25.149391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:26.813268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:28.226654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:29.679497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:31.410939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:32.920028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.311052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:35.747959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:37.116448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:38.754698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:25.310406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:26.979828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:28.345943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:30.015596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:31.551668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:33.074039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.459440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:35.849974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:37.205763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:38.881077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:25.468099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:27.106141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:28.485372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:30.122301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:31.668321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:33.203707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.560092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:36.028409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:37.352392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:39.039030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:25.613478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:27.227587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:28.605755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:30.277356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:31.873211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:33.307701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:34.667793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:36.174629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:36:37.502506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-26T13:36:47.958657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Patient_IDHealth_Camp_IDVar1Var2Var5DonationHealth_ScoreHealth ScoreNumber_of_stall_visitedLast_Stall_Visited_NumberVar3Var4outcomeOnline_FollowerLinkedIn_SharedTwitter_SharedFacebook_SharedIncomeAgeCity_TypeEmployer_CategoryCamp_Start_DateCamp_End_DateCategory1Category2Category3
Patient_ID1.0000.002-0.004-0.005-0.002-0.000-0.015-0.0140.0110.0150.0570.0200.0000.0270.0450.0270.0310.0320.0730.0420.0890.0000.0000.0000.0000.000
Health_Camp_ID0.0021.0000.0490.0300.0460.025-0.003-0.022-0.058-0.0230.0000.0200.2830.0590.0670.0540.0590.0770.0650.0410.0020.9440.9030.5210.5300.489
Var1-0.0040.0491.0000.5650.8740.0400.0650.0030.0250.0130.6120.2840.0430.0970.0940.0360.0420.0560.1970.0420.0680.0180.0190.0080.0100.004
Var2-0.0050.0300.5651.0000.6040.0560.064-0.0040.0150.0220.8230.2790.0320.1060.1200.0390.0490.0520.2560.0390.0670.0150.0150.0000.0090.000
Var5-0.0020.0460.8740.6041.0000.0390.0560.0030.0180.0080.7820.2980.0550.1180.1400.1050.0980.0710.2260.0370.0580.0300.0320.0060.0240.027
Donation-0.0000.0250.0400.0560.0391.0000.421NaNNaNNaN0.0000.0001.0000.0670.0610.0550.0100.0470.1040.0110.0000.0510.0521.0000.1010.014
Health_Score-0.015-0.0030.0650.0640.0560.4211.000NaNNaNNaN0.0000.0061.0000.0000.0000.0610.0510.0350.0370.0370.0420.1160.1161.0000.0680.051
Health Score-0.014-0.0220.003-0.0040.003NaNNaN1.000NaNNaN0.0310.0291.0000.0510.0760.0680.0520.0310.0530.0020.0000.2520.2521.0000.0931.000
Number_of_stall_visited0.011-0.0580.0250.0150.018NaNNaNNaN1.0000.5720.0250.0000.9990.0280.0260.0510.0320.0780.0400.0220.0890.0840.0841.0001.0001.000
Last_Stall_Visited_Number0.015-0.0230.0130.0220.008NaNNaNNaN0.5721.0000.0220.2230.9990.0340.0000.0660.0000.0800.0000.0340.0350.0370.0371.0001.0001.000
Var30.0570.0000.6120.8230.7820.0000.0000.0310.0250.0221.0000.2070.0110.0030.0060.0030.0030.0540.7670.0550.0790.0000.0000.0000.0050.000
Var40.0200.0200.2840.2790.2980.0000.0060.0290.0000.2230.2071.0000.0370.0530.0650.0500.0520.0560.1140.0270.0550.0240.0240.0150.0200.006
outcome0.0000.2830.0430.0320.0551.0001.0001.0000.9990.9990.0110.0371.0000.0500.0570.0450.0400.0870.0990.0200.0410.5180.4170.4720.4810.000
Online_Follower0.0270.0590.0970.1060.1180.0670.0000.0510.0280.0340.0030.0530.0501.0000.4760.6060.4580.3480.3900.0320.1230.0780.0790.0220.0490.011
LinkedIn_Shared0.0450.0670.0940.1200.1400.0610.0000.0760.0260.0000.0060.0650.0570.4761.0000.3820.4730.3850.4180.0270.1300.0900.0930.0220.0550.012
Twitter_Shared0.0270.0540.0360.0390.1050.0550.0610.0680.0510.0660.0030.0500.0450.6060.3821.0000.5160.3390.3830.0470.1170.0690.0720.0190.0420.004
Facebook_Shared0.0310.0590.0420.0490.0980.0100.0510.0520.0320.0000.0030.0520.0400.4580.4730.5161.0000.3500.3980.0310.0870.0770.0780.0230.0490.008
Income0.0320.0770.0560.0520.0710.0470.0350.0310.0780.0800.0540.0560.0870.3480.3850.3390.3501.0000.4090.0740.1030.0990.1010.0370.0840.023
Age0.0730.0650.1970.2560.2260.1040.0370.0530.0400.0000.7670.1140.0990.3900.4180.3830.3980.4091.0000.1090.1720.0400.0420.0270.0800.035
City_Type0.0420.0410.0420.0390.0370.0110.0370.0020.0220.0340.0550.0270.0200.0320.0270.0470.0310.0740.1091.0000.1020.0760.0740.0170.0430.000
Employer_Category0.0890.0020.0680.0670.0580.0000.0420.0000.0890.0350.0790.0550.0410.1230.1300.1170.0870.1030.1720.1021.0000.0120.0110.0340.0160.022
Camp_Start_Date0.0000.9440.0180.0150.0300.0510.1160.2520.0840.0370.0000.0240.5180.0780.0900.0690.0770.0990.0400.0760.0121.0000.9731.0000.9901.000
Camp_End_Date0.0000.9030.0190.0150.0320.0520.1160.2520.0840.0370.0000.0240.4170.0790.0930.0720.0780.1010.0420.0740.0110.9731.0000.8750.9411.000
Category10.0000.5210.0080.0000.0061.0001.0001.0001.0001.0000.0000.0150.4720.0220.0220.0190.0230.0370.0270.0170.0341.0000.8751.0001.0000.051
Category20.0000.5300.0100.0090.0240.1010.0680.0931.0001.0000.0050.0200.4810.0490.0550.0420.0490.0840.0800.0430.0160.9900.9411.0001.0000.069
Category30.0000.4890.0040.0000.0270.0140.0511.0001.0001.0000.0000.0060.0000.0110.0120.0040.0080.0230.0350.0000.0221.0001.0000.0510.0691.000

Missing values

2023-01-26T13:36:39.341984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-26T13:36:40.158427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-26T13:36:40.802519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Patient_IDHealth_Camp_IDRegistration_DateVar1Var2Var3Var4Var5outcomeOnline_FollowerLinkedIn_SharedTwitter_SharedFacebook_SharedIncomeEducation_ScoreAgeFirst_InteractionCity_TypeEmployer_CategoryCamp_Start_DateCamp_End_DateCategory1Category2Category3DonationHealth_ScoreHealth ScoreNumber_of_stall_visitedLast_Stall_Visited_Number
0526927657014/05/050000000000NoneNoneNone14-Nov-04NaNNaN09-Jul-0522-Jul-05FirstE2NaNNaNNaNNaNNaN
1510379653426/05/060000010000NoneNoneNone26-May-06HNaN17-Oct-0507-Nov-07SecondA2NaNNaN0.402054NaNNaN
2520968655707/01/040000010000NoneNoneNone07-Jan-04HNaN04-Jan-0409-Jan-04FirstC220.00.611111NaNNaNNaN
3507625653512/02/040000000000NoneNoneNone12-Feb-04BNaN01-Feb-0418-Feb-04FirstE2NaNNaNNaNNaNNaN
4502611658114/03/040000000000NoneNoneNone14-Mar-04BNaN07-Dec-0313-Jun-04FirstF2NaNNaNNaNNaNNaN
5487442652607/01/050000000000NoneNoneNone30-Nov-04NaNNaN03-Jan-0520-Feb-05FirstE2NaNNaNNaNNaNNaN
6510807654329/12/060000000000NoneNoneNone29-Dec-06NaNNaN27-Sep-0507-Nov-07FirstF2NaNNaNNaNNaNNaN
7502795653922/09/040000000000NoneNoneNone21-Sep-04NaNNaN07-Aug-0412-Feb-05FirstF2NaNNaNNaNNaNNaN
8489318653208/04/050000000000NoneNoneNone24-Jan-05NaNNaN19-Feb-0523-Aug-05FirstF2NaNNaNNaNNaNNaN
9507386654307/11/060000000000NoneNoneNone31-Oct-06NaNNaN27-Sep-0507-Nov-07FirstF2NaNNaNNaNNaNNaN
Patient_IDHealth_Camp_IDRegistration_DateVar1Var2Var3Var4Var5outcomeOnline_FollowerLinkedIn_SharedTwitter_SharedFacebook_SharedIncomeEducation_ScoreAgeFirst_InteractionCity_TypeEmployer_CategoryCamp_Start_DateCamp_End_DateCategory1Category2Category3DonationHealth_ScoreHealth ScoreNumber_of_stall_visitedLast_Stall_Visited_Number
52684516363653203/03/0500000000004535511-Dec-04CNaN19-Feb-0523-Aug-05FirstF2NaNNaNNaNNaNNaN
52685511809652605/11/0400000000000None7203-Oct-04CTechnology03-Jan-0520-Feb-05FirstE2NaNNaNNaNNaNNaN
52686494756654207/04/050000000000NoneNoneNone26-Mar-05NaNNaN19-Feb-0523-Aug-05FirstF2NaNNaNNaNNaNNaN
52687519167654319/07/060000000000NoneNoneNone12-Feb-06BNaN27-Sep-0507-Nov-07FirstF2NaNNaNNaNNaNNaN
52688495320657014/05/053000100000NoneNone4308-Feb-03BNaN09-Jul-0522-Jul-05FirstE2NaNNaNNaNNaNNaN
52689528062652903/03/060000010000NoneNoneNone22-Apr-05HNaN30-Mar-0603-Apr-06SecondA2NaNNaN0.161641NaNNaN
52690513331654610/01/040000000000NoneNoneNone10-Jan-04NaNNaN09-Jan-0417-Jan-04FirstE2NaNNaNNaNNaNNaN
52691486727652330/04/0500000100000None4308-Feb-03INaN23-Feb-0516-Sep-05SecondD2NaNNaN0.518797NaNNaN
52692515312652306/09/0500000100001774601-May-05ANaN23-Feb-0516-Sep-05SecondD2NaNNaN0.964457NaNNaN
52693488938656211/02/050000000000NoneNoneNone31-Jan-05DNaN24-Nov-0402-Jun-05FirstF2NaNNaNNaNNaNNaN